Ltg Bert Babylm

🚀 LTG - BERT 用于 BabyLM 挑战赛

这是在 1亿词 BabyLM 挑战赛数据集 上训练的 LTG - BERT 基线模型。该模型为自然语言处理领域提供了在特定规模数据集上的有效解决方案，具有一定的研究和应用价值。

🚀 快速开始

本项目是在 1亿词 BabyLM 挑战赛数据集 上训练的 LTG - BERT 基线模型。

• 论文：《训练一亿词仍状态良好：BERT 与英国国家语料库相遇》
• GitHub 仓库：[ltgoslo/ltg - bert](https://github.com/ltgoslo/ltg - bert)

📄 许可证

本项目采用 CC - BY - 4.0 许可证。

📚 详细文档

引用说明

如果您使用了本项目，请引用以下出版物：

@inproceedings{samuel-etal-2023-trained,
    title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
    author = "Samuel, David  and
      Kutuzov, Andrey  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.146",
    pages = "1954--1974",
    abstract = "While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source {--} the British National Corpus. We show that pre-training on this carefully curated corpus can reach better performance than the original BERT model. We argue that this type of corpora has great potential as a language modeling benchmark. To showcase this potential, we present fair, reproducible and data-efficient comparative studies of LMs, in which we evaluate several training objectives and model architectures and replicate previous empirical results in a systematic way. We propose an optimized LM architecture called LTG-BERT.",
}

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